Abstract
The multi-view total focusing method (TFM) is an imaging algorithm for ultrasonic full matrix array data that exploits internal reflections and mode conversions in the inspected object to create multiple images, the views. Modelling the defect response in multi-view TFM is an essential first step in developing new detection and characterisation methods which exploit the information present in these views. This paper describes a ray-based forward model for small two-dimensional defects and compares its results against finite-element simulations and experimental data for the inspection of a side-drilled hole, a notch and a crack. A simpler version of this model, based on a single-frequency approximation, is derived and compared. A good agreement with the multi-frequency model and a speed-up of several orders of magnitude are achieved.
Highlights
U LTRASONIC arrays are commonly used in industrial nondestructive evaluation (NDE) for a wide range of applications [1]
Postprocessing the full matrix capture (FMC) data, which contains the time traces corresponding to each pair of transmitter and receiver, is an active area of research
This paper focuses on modeling the ultrasonic response of a small 2-D scatterer and in particular its maximum total focusing method (TFM) intensities across different views
Summary
U LTRASONIC arrays are commonly used in industrial nondestructive evaluation (NDE) for a wide range of applications [1]. Using an amplitude drop measurement technique in multiview TFM images, accurate and reliable sizing of notches larger than two wavelengths was obtained [9], [10]. The aim of the current work is to calculate the TFM intensities of small defects with an even higher computational efficiency. This is an essential first step to develop new defect detection and characterization techniques. This is done by deriving an estimator of the TFM intensities from an analytical model. A simpler and significantly faster single-frequency version is derived for even higher computational efficiency and is compared
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